{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5OP6TQ5NFQ6VPV6HNUSGFUZ7NO","short_pith_number":"pith:5OP6TQ5N","canonical_record":{"source":{"id":"1808.04780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-14T16:17:40Z","cross_cats_sorted":[],"title_canon_sha256":"23863929f9bce5f4714efb31efadb389f30482ae63a067ac3ab3a177ce919b66","abstract_canon_sha256":"45c4ff2bbd89defbe18ccc8a3a9360f921c13e53fd5e53b547a7cdd76a9d65ef"},"schema_version":"1.0"},"canonical_sha256":"eb9fe9c3ad2c3d57d7c76d2462d33f6ba424405a1d28b81bd4d253aed2712bb6","source":{"kind":"arxiv","id":"1808.04780","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04780","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04780v1","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04780","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"pith_short_12","alias_value":"5OP6TQ5NFQ6V","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5OP6TQ5NFQ6VPV6H","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5OP6TQ5N","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5OP6TQ5NFQ6VPV6HNUSGFUZ7NO","target":"record","payload":{"canonical_record":{"source":{"id":"1808.04780","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-14T16:17:40Z","cross_cats_sorted":[],"title_canon_sha256":"23863929f9bce5f4714efb31efadb389f30482ae63a067ac3ab3a177ce919b66","abstract_canon_sha256":"45c4ff2bbd89defbe18ccc8a3a9360f921c13e53fd5e53b547a7cdd76a9d65ef"},"schema_version":"1.0"},"canonical_sha256":"eb9fe9c3ad2c3d57d7c76d2462d33f6ba424405a1d28b81bd4d253aed2712bb6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:11.766720Z","signature_b64":"JWCZoE1RFsJhtED7hG3wPMlOUoL+8kZyovVPKZJ6WIa6WjQm90PbVTcHmszcfsshhvBiwyKy1HatIUyE/w/cDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb9fe9c3ad2c3d57d7c76d2462d33f6ba424405a1d28b81bd4d253aed2712bb6","last_reissued_at":"2026-05-18T00:08:11.766309Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:11.766309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.04780","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:08:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4E10V4GFWIM1dNg38SEfTGw8mDkbOHU1PfU7s0gXL4x09J2Vq4Vbn/0Hr9AxaHfBUNcDCGoY4J6TZG0KrAtFAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:22:22.787019Z"},"content_sha256":"3262003448155c8096a8b96f7d36cbc4fca5a1b64141a998fffe8b42fb09cb0d","schema_version":"1.0","event_id":"sha256:3262003448155c8096a8b96f7d36cbc4fca5a1b64141a998fffe8b42fb09cb0d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5OP6TQ5NFQ6VPV6HNUSGFUZ7NO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multivariate Density Estimation with Missing Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Lili Wu, Sujit Ghosh, Weilian Zhou, Zhen Li","submitted_at":"2018-08-14T16:17:40Z","abstract_excerpt":"Multivariate density estimation is a popular technique in statistics with wide applications including regression models allowing for heteroskedasticity in conditional variances. The estimation problems become more challenging when observations are missing in one or more variables of the multivariate vector. A flexible class of mixture of tensor products of kernel densities is proposed which allows for easy implementation of imputation methods using Gibbs sampling and shown to have superior performance compared to some of the exisiting imputation methods currently available in literature. Numer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04780","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:08:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rQuZuhyQ5b32sFHjgHixw3T8M2tgyWbvYNcjWl6eMpj18EuZKh42r34GVUw++Yvu554ap62SeCbasZPuQWMbCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:22:22.787481Z"},"content_sha256":"42425d01245fbf6276da891b70b395623478ace287c857fd2cb07e6449673908","schema_version":"1.0","event_id":"sha256:42425d01245fbf6276da891b70b395623478ace287c857fd2cb07e6449673908"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/bundle.json","state_url":"https://pith.science/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-28T21:22:22Z","links":{"resolver":"https://pith.science/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO","bundle":"https://pith.science/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/bundle.json","state":"https://pith.science/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5OP6TQ5NFQ6VPV6HNUSGFUZ7NO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5OP6TQ5NFQ6VPV6HNUSGFUZ7NO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"45c4ff2bbd89defbe18ccc8a3a9360f921c13e53fd5e53b547a7cdd76a9d65ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-14T16:17:40Z","title_canon_sha256":"23863929f9bce5f4714efb31efadb389f30482ae63a067ac3ab3a177ce919b66"},"schema_version":"1.0","source":{"id":"1808.04780","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04780","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04780v1","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04780","created_at":"2026-05-18T00:08:11Z"},{"alias_kind":"pith_short_12","alias_value":"5OP6TQ5NFQ6V","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5OP6TQ5NFQ6VPV6H","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5OP6TQ5N","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:42425d01245fbf6276da891b70b395623478ace287c857fd2cb07e6449673908","target":"graph","created_at":"2026-05-18T00:08:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Multivariate density estimation is a popular technique in statistics with wide applications including regression models allowing for heteroskedasticity in conditional variances. The estimation problems become more challenging when observations are missing in one or more variables of the multivariate vector. A flexible class of mixture of tensor products of kernel densities is proposed which allows for easy implementation of imputation methods using Gibbs sampling and shown to have superior performance compared to some of the exisiting imputation methods currently available in literature. Numer","authors_text":"Lili Wu, Sujit Ghosh, Weilian Zhou, Zhen Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-14T16:17:40Z","title":"Multivariate Density Estimation with Missing Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04780","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3262003448155c8096a8b96f7d36cbc4fca5a1b64141a998fffe8b42fb09cb0d","target":"record","created_at":"2026-05-18T00:08:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"45c4ff2bbd89defbe18ccc8a3a9360f921c13e53fd5e53b547a7cdd76a9d65ef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-14T16:17:40Z","title_canon_sha256":"23863929f9bce5f4714efb31efadb389f30482ae63a067ac3ab3a177ce919b66"},"schema_version":"1.0","source":{"id":"1808.04780","kind":"arxiv","version":1}},"canonical_sha256":"eb9fe9c3ad2c3d57d7c76d2462d33f6ba424405a1d28b81bd4d253aed2712bb6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eb9fe9c3ad2c3d57d7c76d2462d33f6ba424405a1d28b81bd4d253aed2712bb6","first_computed_at":"2026-05-18T00:08:11.766309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:11.766309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JWCZoE1RFsJhtED7hG3wPMlOUoL+8kZyovVPKZJ6WIa6WjQm90PbVTcHmszcfsshhvBiwyKy1HatIUyE/w/cDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:11.766720Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.04780","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3262003448155c8096a8b96f7d36cbc4fca5a1b64141a998fffe8b42fb09cb0d","sha256:42425d01245fbf6276da891b70b395623478ace287c857fd2cb07e6449673908"],"state_sha256":"5befaef21c4c7039c8090a0b60ef9b8a995f882191ebf6ebe14922d7484909bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LfqwvALSytUlf5OcofOwgIQk9Zl8XkZE70MxlP8VdDQAuLK4K86EqyZ5LnwILNWa6QRFmzMhsmBNI7I17Z8EBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:22:22.791020Z","bundle_sha256":"696c9285cfb75eb8590aebe41abd434866e6b9a764fbd10277ed7322842dff25"}}